permaculture

[Lawrence London <lfljvenaura@gmail.com>]

https://www.nature.com/articles/ncomms13630?fbclid=IwAR0LJUwYyZe9DNkyb4gJtuftQzep9LTUdGHZ0ksFQdG2oXs2_k3uqU7-89U#results

Direct evidence for microbial-derived soil organic matter formation and its
ecophysiological controls

   - Cynthia M. Kallenbach
   
<https://www.nature.com/articles/ncomms13630?fbclid=IwAR0LJUwYyZe9DNkyb4gJtuftQzep9LTUdGHZ0ksFQdG2oXs2_k3uqU7-89U#auth-1>
   - , Serita D. Frey
   
<https://www.nature.com/articles/ncomms13630?fbclid=IwAR0LJUwYyZe9DNkyb4gJtuftQzep9LTUdGHZ0ksFQdG2oXs2_k3uqU7-89U#auth-2>
   -  & A. Stuart Grandy
   
<https://www.nature.com/articles/ncomms13630?fbclid=IwAR0LJUwYyZe9DNkyb4gJtuftQzep9LTUdGHZ0ksFQdG2oXs2_k3uqU7-89U#auth-3>

*Nature Communications* *volume 7*, Article number: 13630 (2016) | Download
Citation <https://www.nature.com/articles/ncomms13630.ris>

   - An Author Correction
   <https://www.nature.com/articles/s41467-018-06427-3> to this article was
   published on 24 September 2018

This article has been updated
<https://www.nature.com/articles/ncomms13630?fbclid=IwAR0LJUwYyZe9DNkyb4gJtuftQzep9LTUdGHZ0ksFQdG2oXs2_k3uqU7-89U#change-history>
Abstract

Soil organic matter (SOM) and the carbon and nutrients therein drive
fundamental submicron- to global-scale biogeochemical processes and
influence carbon-climate feedbacks. Consensus is emerging that microbial
materials are an important constituent of stable SOM, and new conceptual
and quantitative SOM models are rapidly incorporating this view. However,
direct evidence demonstrating that microbial residues account for the
chemistry, stability and abundance of SOM is still lacking. Further,
emerging models emphasize the stabilization of microbial-derived SOM by
abiotic mechanisms, while the effects of microbial physiology on microbial
residue production remain unclear. Here we provide the first direct
evidence that soil microbes produce chemically diverse, stable SOM. We show
that SOM accumulation is driven by distinct microbial communities more so
than clay mineralogy, where microbial-derived SOM accumulation is greatest
in soils with higher fungal abundances and more efficient microbial biomass
production.
Introduction

For nearly a century, soil organic matter (SOM) formation in conceptual and
quantitative models has been depicted primarily as a function of plant
inputs and their chemistry1
<https://www.nature.com/articles/ncomms13630#ref1>,2
<https://www.nature.com/articles/ncomms13630#ref2>,3
<https://www.nature.com/articles/ncomms13630#ref3>. As such, chemically
diverse and stable SOM originates from the preservation of biochemically
recalcitrant complex plant polymers, such as lignin derivatives and
long-chain lipids2 <https://www.nature.com/articles/ncomms13630#ref2>,3
<https://www.nature.com/articles/ncomms13630#ref3>. However, soil microbial
communities are adept at decomposing a wide range of plant compounds and
using the carbon (C) to synthesize their own biomass. The importance of
soil microbes in processing plant inputs and synthesizing SOM is not
conceptually new4 <https://www.nature.com/articles/ncomms13630#ref4>,5
<https://www.nature.com/articles/ncomms13630#ref5>,6
<https://www.nature.com/articles/ncomms13630#ref6>, though until recently
it has largely been overlooked as a primary pathway of SOM formation7
<https://www.nature.com/articles/ncomms13630#ref7>. In a significant
departure from the dominant plant-based models of the past, microbial
contributions to SOM formation have gained widespread acceptance, in part
due to advances in molecular analytical techniques1
<https://www.nature.com/articles/ncomms13630#ref1>,7
<https://www.nature.com/articles/ncomms13630#ref7>,8
<https://www.nature.com/articles/ncomms13630#ref8>. Mounting evidence
demonstrates that some decomposition-resistant SOM bears little chemical
resemblance to plant material, but is instead characteristic of microbial
cells, excretions and cytoplasmic materials 9
<https://www.nature.com/articles/ncomms13630#ref9>,10
<https://www.nature.com/articles/ncomms13630#ref10>,11
<https://www.nature.com/articles/ncomms13630#ref11> stabilized via
organo-mineral and organo-metal oxide interactions12
<https://www.nature.com/articles/ncomms13630#ref12>,13
<https://www.nature.com/articles/ncomms13630#ref13>. While the evidence to
support this alternative pathway of SOM formation is compelling8
<https://www.nature.com/articles/ncomms13630#ref8>,9
<https://www.nature.com/articles/ncomms13630#ref9>,10
<https://www.nature.com/articles/ncomms13630#ref10>,11
<https://www.nature.com/articles/ncomms13630#ref11>,14
<https://www.nature.com/articles/ncomms13630#ref14>,15
<https://www.nature.com/articles/ncomms13630#ref15>, analytical challenges
associated with separating direct microbial and plant inputs to SOM create
fundamental uncertainties about the degree to which microbial residues
contribute to the formation of stable SOM and its characteristic chemical
diversity11 <https://www.nature.com/articles/ncomms13630#ref11>,16
<https://www.nature.com/articles/ncomms13630#ref16>,17
<https://www.nature.com/articles/ncomms13630#ref17>.

Whether plant inputs are first converted to microbial residues before
stabilization influences how SOM responds to land use and climate change1
<https://www.nature.com/articles/ncomms13630#ref1>,18
<https://www.nature.com/articles/ncomms13630#ref18>,19
<https://www.nature.com/articles/ncomms13630#ref19> as well as how it
should be modelled20 <https://www.nature.com/articles/ncomms13630#ref20>
and managed to promote climate change mitigation21
<https://www.nature.com/articles/ncomms13630#ref21>. Plant residues that
accumulate in soil through physical protection (e.g., inside aggregates) or
in zones with low biological activity are susceptible to destabilization
following disturbances such as cultivation or in response to environmental
change (e.g., temperature increases)19
<https://www.nature.com/articles/ncomms13630#ref19>,22
<https://www.nature.com/articles/ncomms13630#ref22>. If, however, plant
materials are synthesized into microbial proteins, lipids or
polysaccharides, the resulting organo-mineral associations may include
ligand bonds or other strong interactions22
<https://www.nature.com/articles/ncomms13630#ref22> that have lower
temperature sensitivity13
<https://www.nature.com/articles/ncomms13630#ref13>,23
<https://www.nature.com/articles/ncomms13630#ref23> and may better
withstand perturbations. Despite the potential importance of microbially
derived SOM, experimental evidence for microbial contributions to SOM
formation is constrained by methods that select for a limited group of
microbial biomarkers, such as amino sugars or select lipids9
<https://www.nature.com/articles/ncomms13630#ref9>,21
<https://www.nature.com/articles/ncomms13630#ref21>, is typically
correlative and inferential9
<https://www.nature.com/articles/ncomms13630#ref9>,21
<https://www.nature.com/articles/ncomms13630#ref21>,24
<https://www.nature.com/articles/ncomms13630#ref24>,25
<https://www.nature.com/articles/ncomms13630#ref25>, or relies on
visualization techniques that cannot easily be scaled up to a whole-soil
basis10 <https://www.nature.com/articles/ncomms13630#ref10>. These
constraints have hindered our ability to quantitatively determine the
importance of microbial-derived compounds as key proximate inputs to SOM,
and thus limit our understanding, management and predictions of SOM
dynamics.

Further, many newly developed conceptual and quantitative microbial models
put considerable emphasis on the abiotic stabilization20
<https://www.nature.com/articles/ncomms13630#ref20>,26
<https://www.nature.com/articles/ncomms13630#ref26>,27
<https://www.nature.com/articles/ncomms13630#ref27> of microbial-derived
SOM, yet the ecological controls that regulate the transfer of microbial
residues to mineral-associated SOM have yet to be resolved20
<https://www.nature.com/articles/ncomms13630#ref20>. While clay mineralogy
is known to regulate microbial-SOM accumulation22
<https://www.nature.com/articles/ncomms13630#ref22>,28
<https://www.nature.com/articles/ncomms13630#ref28>, microbial community
structure and physiology likely also determine stable SOM accumulation
rates due to differences in microbial residue production15
<https://www.nature.com/articles/ncomms13630#ref15>,21
<https://www.nature.com/articles/ncomms13630#ref21>,29
<https://www.nature.com/articles/ncomms13630#ref29>. For example, microbial
carbon use efficiency (CUE), the amount of C used for microbial growth
relative to total C uptake may have a direct impact on microbial residue
production15 <https://www.nature.com/articles/ncomms13630#ref15>,21
<https://www.nature.com/articles/ncomms13630#ref21> but can differ across
resource gradients30 <https://www.nature.com/articles/ncomms13630#ref30>
and microbial communities31
<https://www.nature.com/articles/ncomms13630#ref31>. As CUE increases,
relatively more substrate-C goes towards biomass synthesis, potentially
increasing the amount of residues available for stabilization21
<https://www.nature.com/articles/ncomms13630#ref21>,29
<https://www.nature.com/articles/ncomms13630#ref29>. Substrate chemistry
can alter CUE due to its direct effect on cellular metabolism, but may also
drive CUE indirectly, by selecting for distinct microbial communities with
different prevailing life histories31
<https://www.nature.com/articles/ncomms13630#ref31>. For instance, highly
reduced substrate-C (high free energy) typically promotes higher CUE within
a community but may also select for a copiotrophic-dominated community with
an inherently lower CUE31
<https://www.nature.com/articles/ncomms13630#ref31>,32
<https://www.nature.com/articles/ncomms13630#ref32>. Thus, microbial
community composition, available substrates and CUE are intimately
connected, but the influence of their interactions on microbial-SOM
formation has not been well characterized32
<https://www.nature.com/articles/ncomms13630#ref32>.

We use model soils to quantitatively assess whether microbial processing of
simple C (i.e., low-molecular-weight) substrates alone, in the absence of
complex plant compounds, can build significant amounts of chemically
diverse, stable SOM. Since our model soils are initially C- and
microbe-free, we eliminate the difficulties of isolating microbial residues
that occur when using natural soils. We use a gradient of substrate-C
inputs to represent different microbial-available energy in order to
facilitate the development of diverging microbial communities and
physiologies that are hypothesized to influence SOM chemistry and
accumulation rates. The substrate gradient includes monomeric and dimeric
sugars since they are abundant energy sources for microbial metabolism in
natural soils33 <https://www.nature.com/articles/ncomms13630#ref33>, but
also because their rapid microbial uptake and intercellular breakdown
should leave little to no unaltered substrate in the soil that would
interfere with the detection of novel SOM molecules formed during the
experiment33 <https://www.nature.com/articles/ncomms13630#ref33>,34
<https://www.nature.com/articles/ncomms13630#ref34>. Further, we include a
more recalcitrant lignin monomeric substrate, as well as plant-derived
dissolved organic C (DOC), a natural analog to test whether substrate
chemical diversity is requisite to generating SOM chemical diversity. We
also compare two clay types (kaolinite or montmorillonite) to investigate
the effects of clay mineralogy on SOM accumulation relative to microbial
communities and substrate chemistry. We characterize the composition of SOM
after 18 months of incubation using high-resolution molecular
fingerprinting by pyrolysis-gas chromatography/mass spectrometry (py-GC/MS)
to establish the chemical fingerprint of newly formed microbial residues
(including cell wall and cytoplasmic materials, metabolites and
extracellular excretions). Our model soils accrue C concentrations between
1 and 1.4% C, with a chemical diversity and stability characteristic of
natural soils. Substrate type has a stronger influence on SOM development
than clay mineralogy. However, this effect appears to be an indirect
consequence of diverging microbial communities, where different substrates
select for distinct microbial communities, with microbial-SOM accumulation
being greatest in soils where fungal abundances are highest and microbial
biomass production is most efficient.
Results
Model soil systems

We created initially C- and microbe-free model soils to study proximal
drivers of SOM development. Model soils were incubated with a natural soil
microbial community inoculum and received weekly C additions of glucose,
cellobiose, syringol (a lignin monomer) or plant-derived DOC, combined with
a nutrient solution for 15 months. Substrate additions were terminated
after 15 months, but the incubation continued for an additional 3 months
(18 months total) without further C inputs in order to maximize endogenous
C recycling.

Following inoculation of model soils, microbial activity was immediately
detectable by a measurable CO2 efflux, and by 6 months post-inoculation
microbial extracellular enzymatic activities, CO2 respiration rates and
living microbial biomass all indicated that an active microbial community
had been established in all treatments (Supplementary Fig. 1
<https://www.nature.com/articles/ncomms13630#s1>). Visually, model soils
progressively resembled natural soil, with significant colour and
structural development (Fig. 1a
<https://www.nature.com/articles/ncomms13630?fbclid=IwAR0LJUwYyZe9DNkyb4gJtuftQzep9LTUdGHZ0ksFQdG2oXs2_k3uqU7-89U#f1>).
The exception to this was the kaolinite–syringol treatment, which after the
initial 3 months did not exhibit an active microbial community and was thus
removed from the study.
*Figure 1: Soil development and organic matter chemistry.*
[image: Figure 1] <https://www.nature.com/articles/ncomms13630/figures/1>

Images of sugar-treated model soils over time (*a*); the far left panel is
an uninoculated sterile kaolinite and sand mixture, and the far right panel
is the same mixture, inoculated and treated with weekly glucose additions
for 15 months. Relative abundance of chemical compound groups in substrate
(Time 0) and model soils amended with (*b*) sugar, (*c*) syringol and (*d*)
plant dissolved organic carbon (DOC). These are compared to soil collected
from an agricultural field (*e*). Glucose and cellobiose treatments were
averaged since there were no significant differences in their chemistry
(ANOVA: *P*>0.05). Numbers above bars are the total number of identified
compounds.
Full size image <https://www.nature.com/articles/ncomms13630/figures/1>

*SOM chemistry*. We used py-GC/MS to examine the chemistry of added
substrates, along with SOM chemistry at the termination of the study (18
months). Substrate chemistry at Time 0 provides a control to compare
changes in SOM chemistry following microbial processing. The visual changes
we observed in soil development (Fig. 1a
<https://www.nature.com/articles/ncomms13630?fbclid=IwAR0LJUwYyZe9DNkyb4gJtuftQzep9LTUdGHZ0ksFQdG2oXs2_k3uqU7-89U#f1>)
coincided with the creation of microbial residues, specifically
microbial-derived proteins and lipids (Fig. 1b–d
<https://www.nature.com/articles/ncomms13630?fbclid=IwAR0LJUwYyZe9DNkyb4gJtuftQzep9LTUdGHZ0ksFQdG2oXs2_k3uqU7-89U#f1>).
After 18 months, regardless of initial substrate chemistry or clay type,
accumulated SOM largely consisted of microbial products (Fig. 1
<https://www.nature.com/articles/ncomms13630?fbclid=IwAR0LJUwYyZe9DNkyb4gJtuftQzep9LTUdGHZ0ksFQdG2oXs2_k3uqU7-89U#f1>),
and the SOM molecular diversity (number of compounds) increased by 54–83%
across our model soil systems, with the number of novel compounds varying
from 73 to 100% of the total (Supplementary Fig. 2
<https://www.nature.com/articles/ncomms13630#s1>).

Initially, the unprocessed glucose and cellobiose substrates (Time 0)
contained primarily polysaccharides (80% relative abundance) while the
syringol substrate was largely comprised of lignin derivatives (80%
relative abundance) (Fig. 1
<https://www.nature.com/articles/ncomms13630?fbclid=IwAR0LJUwYyZe9DNkyb4gJtuftQzep9LTUdGHZ0ksFQdG2oXs2_k3uqU7-89U#f1>;
Supplementary Note 1 <https://www.nature.com/articles/ncomms13630#s1>).
After 18 months, polysaccharide relative abundance in sugar-treated soils
declined to 18%. Similarly, in syringol-treated soils, lignin derivatives
declined to 1% over that time period. In both the sugar- and
syringol-treated soils, the dominant chemical signature of the substrate
was replaced by proteins, non-proteinaceous nitrogen compounds and lipids.
Lipids and proteins, not initially present in unprocessed substrates,
collectively made up 37% relative abundance of all compounds in newly
formed SOM (Fig. 1
<https://www.nature.com/articles/ncomms13630?fbclid=IwAR0LJUwYyZe9DNkyb4gJtuftQzep9LTUdGHZ0ksFQdG2oXs2_k3uqU7-89U#f1>
and Supplementary Table 1 <https://www.nature.com/articles/ncomms13630#s1>).
Chitin, originating primarily from fungal cell walls6
<https://www.nature.com/articles/ncomms13630#ref6>, also increased by 5%
after 18 months. We also observed increases in both the aromatic and
unspecified compound classes after 18 months across all soils. The most
abundant compounds within these classes are consistent with abundant
compounds observed in fungal biomass derived from natural soil (Supplementary
Table 2 <https://www.nature.com/articles/ncomms13630#s1> and Supplementary
Note 1 <https://www.nature.com/articles/ncomms13630#s1>).

Microbial processing of sugars produced SOM similar in chemistry and
diversity to soils amended with more recalcitrant syringol, and began to
resemble soils treated with more heterogeneous plant-derived DOC, as well
as a natural soil (Figs 1
<https://www.nature.com/articles/ncomms13630?fbclid=IwAR0LJUwYyZe9DNkyb4gJtuftQzep9LTUdGHZ0ksFQdG2oXs2_k3uqU7-89U#f1>
and 2
<https://www.nature.com/articles/ncomms13630?fbclid=IwAR0LJUwYyZe9DNkyb4gJtuftQzep9LTUdGHZ0ksFQdG2oXs2_k3uqU7-89U#f2>).
Further, microbial processing of DOC also increased chemical diversity by
54% (Fig. 1
<https://www.nature.com/articles/ncomms13630?fbclid=IwAR0LJUwYyZe9DNkyb4gJtuftQzep9LTUdGHZ0ksFQdG2oXs2_k3uqU7-89U#f1>).
In contrast to traditional expectations that slowly decomposing plant
compounds accumulate in SOM2
<https://www.nature.com/articles/ncomms13630#ref2>,3
<https://www.nature.com/articles/ncomms13630#ref3>, lignin derivatives,
specific to plant-derived lignin macromolecules, declined from 10 to 2.5%
relative abundance in DOC-treated soils, and proteins and lipids increased
five-fold (*P*<0.05).
*Figure 2: Differences in soil organic matter chemistry between substrate
and clay types.*
[image: Figure 2] <https://www.nature.com/articles/ncomms13630/figures/2>

Non-metric multidimensional scaling (NMDS) ordination of the relative
abundance of chemistry compounds at 18 months for substrate and clay
treatments. Open symbols are kaolinite and closed symbols are
montmorillonite. For comparison, unprocessed substrates (Time 0) and an
agricultural field soil are indicated by a star symbol (Stress=8.1, Monte
Carlo: *P*<0.05).
Full size image <https://www.nature.com/articles/ncomms13630/figures/2>

Significant differences in SOM chemistry due to mineralogy emerged after 18
months (Monte Carlo *P*<0.05; multi-response permutation procedure *P*<0.05)
(Fig. 2
<https://www.nature.com/articles/ncomms13630?fbclid=IwAR0LJUwYyZe9DNkyb4gJtuftQzep9LTUdGHZ0ksFQdG2oXs2_k3uqU7-89U#f2>
and Supplementary Tables 1 and 3
<https://www.nature.com/articles/ncomms13630#s1>). For example, kaolinite
sugar-treated soils had higher relative abundances of polysaccharides and
chitin derivatives and a lower abundance of aromatic compounds compared
with montmorillonite (*P*<0.05). However, there was little influence of the
substrate treatment on final SOM chemistry, with, for instance,
montmorillonite, sugar- and syringol-treated soils exhibiting similar SOM
chemistries at 18 months (Fig. 2
<https://www.nature.com/articles/ncomms13630?fbclid=IwAR0LJUwYyZe9DNkyb4gJtuftQzep9LTUdGHZ0ksFQdG2oXs2_k3uqU7-89U#f2>
).
SOM accumulation and stability

We determined soil organic carbon (SOC) accumulation rates and stability in
the initially C-free model soils. Across all model soils, SOC increased
consistently over time (*P*<0.05; Table 1
<https://www.nature.com/articles/ncomms13630?fbclid=IwAR0LJUwYyZe9DNkyb4gJtuftQzep9LTUdGHZ0ksFQdG2oXs2_k3uqU7-89U#t1>).
The efficiency with which substrate-C was converted to total SOC—i.e., the
amount of SOC remaining relative to the total amount of substrate
added—declined from an average across all treatments of 32% ±1.4 at 6
months to 21% ±0.75 at 18 months Using SOC stocks as an integrator of mass
C balance, the majority (>75%) of total substrate-C added was lost via
respiration across all treatments by 18 months (Table 1
<https://www.nature.com/articles/ncomms13630?fbclid=IwAR0LJUwYyZe9DNkyb4gJtuftQzep9LTUdGHZ0ksFQdG2oXs2_k3uqU7-89U#t1>).
As a comparison, estimated cumulative respirations represented 50–75% of
added C with the exception of DOC-treated soils (Supplementary Table 4
<https://www.nature.com/articles/ncomms13630#s1>). These estimates are
based on flux measurements, which exhibited rapid and ephemeral responses
to each substrate addition, especially for the monomeric inputs. In
DOC-treated soils, cumulative respiration underestimated C loss, compared
with calculations using SOC stocks. We attribute this to the more
chemically complex DOC treatment having a longer respiration response time
that was not completely captured from fluxes collected immediately
following substrate additions.
*Table 1: Soil carbon accumulation.*
Full size table <https://www.nature.com/articles/ncomms13630/tables/1>

The final total soil C concentrations at the end of the C addition period
(1–1.4%) are well within the range of many natural soils21
<https://www.nature.com/articles/ncomms13630#ref21> (Table 1
<https://www.nature.com/articles/ncomms13630?fbclid=IwAR0LJUwYyZe9DNkyb4gJtuftQzep9LTUdGHZ0ksFQdG2oXs2_k3uqU7-89U#t1>).
The syringol-treated montmorillonite soils accumulated the most C (*P*<0.001);
however, there were no differences in SOC among the glucose- or
cellobiose-treated soils in either clay type. DOC-treated soils had higher
SOC compared with sugar-treated soils (12.8 mg C per g soil, *P*<0.001),
and also exhibited differences among clay types (*P*<0.05; Supplementary
Table 3 <https://www.nature.com/articles/ncomms13630#s1>).

We determined SOC biological and chemical stability to evaluate its
potential long-term persistence. We assessed biological stability by adding
a 13C-labelled substrate mixture (1:1 glutamic acid:glucose at 25 atom %
and 50 μg C per g soil) to a subsample of soil from the main experiment and
incubating for 3 months (Table 2
<https://www.nature.com/articles/ncomms13630?fbclid=IwAR0LJUwYyZe9DNkyb4gJtuftQzep9LTUdGHZ0ksFQdG2oXs2_k3uqU7-89U#t2>).
The labelled substrate enabled us to use a standard isotope mixing model35
<https://www.nature.com/articles/ncomms13630#ref35> to determine the amount
of previously formed C vulnerable to decomposition by an active microbial
community. Chemical stability of accumulated SOC was assessed with an acid
hydrolysis fractionation, where the unhydrolysable fraction is considered
chemically stable36 <https://www.nature.com/articles/ncomms13630#ref36>.
The majority of SOC was biologically stable (71% ±2.5; Table 2
<https://www.nature.com/articles/ncomms13630?fbclid=IwAR0LJUwYyZe9DNkyb4gJtuftQzep9LTUdGHZ0ksFQdG2oXs2_k3uqU7-89U#t2>)
and 40% ±1 was chemically stable to oxidation, which is within the upper
range observed for natural soils36
<https://www.nature.com/articles/ncomms13630#ref36>,37
<https://www.nature.com/articles/ncomms13630#ref37>. We observed greater
biological stability in montmorillonite than kaolinite model soils only
within the cellobiose and DOC treatments (*P*<0.05; Supplementary Table 3
<https://www.nature.com/articles/ncomms13630#s1>).
*Table 2: Percentage of stable soil carbon.*
Full size table <https://www.nature.com/articles/ncomms13630/tables/2>
Microbial community composition and physiology

Given its potential influence on SOM accumulation rates and chemistry, we
examined microbial community composition using phospholipid fatty acid
(PLFA) biomarkers. We observed significant differences in broad microbial
groups due to substrate treatment (*P*<0.05; Supplementary Table 3
<https://www.nature.com/articles/ncomms13630#s1> and Supplementary Fig. 3
<https://www.nature.com/articles/ncomms13630#s1>). Montmorillonite,
syringol-treated soils had lower Gram-negative and Gram-positive bacteria
and higher fungal relative abundance compared with sugar-treated soils
(*P*<0.001;
Fig. 3
<https://www.nature.com/articles/ncomms13630?fbclid=IwAR0LJUwYyZe9DNkyb4gJtuftQzep9LTUdGHZ0ksFQdG2oXs2_k3uqU7-89U#f3>).
We also observed an effect of mineralogy on the microbial community; fungal
abundances were generally higher in kaolinite than in montmorillonite
soils, while Gram-negative bacteria were more abundant in montmorillonite
soils (*P*<0.001).
*Figure 3: Influence of substrate and clay type on soil microbial
communities and microbial growth efficiency.*
[image: Figure 3] <https://www.nature.com/articles/ncomms13630/figures/3>

Substrate treatment differences in the relative abundance (RA) of fungi (*a*),
Gram-negative bacteria (*b*), Gram-positive bacteria (*c*) at 15 months,
and microbial carbon use efficiency (CUE) for kaolinite (*d*) and
montmorillonite (*e*) model soils. The CUE values (*d*,*e*) are for 9 and
15 months of incubation. Significant substrate treatment effects are within
clay type or within time and are indicated by different letters
(ANOVA: *P*<0.05).
Error bars represent one s.e. (experimental replication *n*=5).
Full size image <https://www.nature.com/articles/ncomms13630/figures/3>

We evaluated community-level CUE to determine whether the distinct
community composition we observed between substrates was related to
differences in microbial physiology and subsequently SOC accumulation. We
estimated the CUE using 13C-labelled glutamic acid uptake into microbial
biomass and respired 13C-CO2 as a proxy for determining the proportion of
new C inputs used for microbial biomass synthesis30
<https://www.nature.com/articles/ncomms13630#ref30>,32
<https://www.nature.com/articles/ncomms13630#ref32>. There was a strong
effect of substrate type, where syringol-treated soils exhibited the
highest CUE (*P*<0.001; Fig. 3
<https://www.nature.com/articles/ncomms13630?fbclid=IwAR0LJUwYyZe9DNkyb4gJtuftQzep9LTUdGHZ0ksFQdG2oXs2_k3uqU7-89U#f3>).
Microbial community composition, CUE and SOC accumulation were highly
correlated across substrate treatments (Fig. 4
<https://www.nature.com/articles/ncomms13630?fbclid=IwAR0LJUwYyZe9DNkyb4gJtuftQzep9LTUdGHZ0ksFQdG2oXs2_k3uqU7-89U#f4>).
Soils with the highest SOC accumulation exhibited higher fungal relative
abundances and CUE (Fig. 4a,c,d
<https://www.nature.com/articles/ncomms13630?fbclid=IwAR0LJUwYyZe9DNkyb4gJtuftQzep9LTUdGHZ0ksFQdG2oXs2_k3uqU7-89U#f4>).
Accordingly, higher fungal abundance was also positively correlated with
CUE (Fig. 4b
<https://www.nature.com/articles/ncomms13630?fbclid=IwAR0LJUwYyZe9DNkyb4gJtuftQzep9LTUdGHZ0ksFQdG2oXs2_k3uqU7-89U#f4>).
Though syringol-treated soils exhibited the highest CUE, fungal relative
abundances and SOC, the relationships among these variables remain when we
analysed only sugar-treated soils (Supplementary Fig. 4
<https://www.nature.com/articles/ncomms13630#s1>). We also observed that
soils with higher SOC concentrations had greater lipid relative abundances
and lower protein abundances (Fig. 4a
<https://www.nature.com/articles/ncomms13630?fbclid=IwAR0LJUwYyZe9DNkyb4gJtuftQzep9LTUdGHZ0ksFQdG2oXs2_k3uqU7-89U#f4>
).
*Figure 4: Relationships between soil organic matter and microbial
variables.*
[image: Figure 4] <https://www.nature.com/articles/ncomms13630/figures/4>

Non-metric multidimensional scaling (NMDS) ordination of accumulated SOC at
12, 15 and 18 months for sugar- and syringol-treated soils (*a*) and
Pearson correlations for fungal relative abundances and microbial carbon
use efficiency (CUE) (*b*), SOC and CUE (*c*), and fungal relative
abundances and SOC (*d*) at 15 months. NMDS points are categorized *post
hoc* based on soil C concentrations at 18 months: high SOC (closed
circles), medium SOC (closed squares) and low SOC (closed triangles)
(Stress=4.2, Monte Carlo: *P*=0.019, multi-response permutation procedure
for SOC groups: *T*=−10.037, *A*=0,469, *P*<0.0001). NMDS table inset is
Pearson correlation *r* values with ordination Axis 1.
Full size image <https://www.nature.com/articles/ncomms13630/figures/4>
Discussion

Model soil systems provide a platform for directly manipulating specific
abiotic and biotic controls on soil processes. They allow for insight into
fundamental questions about soil aggregate development38
<https://www.nature.com/articles/ncomms13630#ref38>, organic matter turnover
39 <https://www.nature.com/articles/ncomms13630#ref39> and mineralogical
influences on microbial communities and decomposition40
<https://www.nature.com/articles/ncomms13630#ref40>,41
<https://www.nature.com/articles/ncomms13630#ref41>,42
<https://www.nature.com/articles/ncomms13630#ref42>. Here we use model
soils for real-time monitoring of microbial-SOM formation and demonstrate
that microbial processing of simple C substrates produced an abundance of
stable, chemically diverse SOM dominated by microbial proteins and lipids,
comparable to natural soils. However, clay mineralogy, a well-known control
over SOM dynamics13 <https://www.nature.com/articles/ncomms13630#ref13>,28
<https://www.nature.com/articles/ncomms13630#ref28>, had little effect on
SOM abundance and stability. Instead the microbial community and its
associated physiology was a stronger driver of SOM development. This
suggests that the community’s physiology and their residue inputs to SOM
may be as or more important than the more widely recognized effects of soil
mineral structure on soil C stabilization.

Considerable SOM accumulated (8–13 mg C per g soil) within 18 months in our
model soils (Table 1
<https://www.nature.com/articles/ncomms13630?fbclid=IwAR0LJUwYyZe9DNkyb4gJtuftQzep9LTUdGHZ0ksFQdG2oXs2_k3uqU7-89U#t1>).
The high microbial activity (Supplementary Fig. 1
<https://www.nature.com/articles/ncomms13630#s1>), cumulative
respiration (Supplementary
Table 4 <https://www.nature.com/articles/ncomms13630#s1>) and especially
SOM chemistry (Fig. 1
<https://www.nature.com/articles/ncomms13630?fbclid=IwAR0LJUwYyZe9DNkyb4gJtuftQzep9LTUdGHZ0ksFQdG2oXs2_k3uqU7-89U#f1>)
in our model soils indicate that substrates were first processed by
microbes before becoming SOM. Further, consistent with studies showing
rapid microbial assimilation of low molecular weight compounds33
<https://www.nature.com/articles/ncomms13630#ref33>, including glucose34
<https://www.nature.com/articles/ncomms13630#ref34>,43
<https://www.nature.com/articles/ncomms13630#ref43>,44
<https://www.nature.com/articles/ncomms13630#ref44>, we found that the
three most abundant compounds in unprocessed sugar and syringol declined by
87 and 95%, respectively, following incubation. Moreover, the protein,
lipid and chitin classes derived exclusively from microbes constituted 34%
of the SOM compound relative abundance across all samples. Many of the
compounds not derived from lipids or proteins may also be derived from
microbial residues. For example, we know that microbial residues generate a
variety of extracellular polysaccharides, and peptidoglycan is a primary
cell wall building block6 <https://www.nature.com/articles/ncomms13630#ref6>
,43 <https://www.nature.com/articles/ncomms13630#ref43>. We also found that
the unspecified compounds toluene, cyclooctatetraene and the aromatic
*m*-xylene
are the most abundant compounds within their class in the sugar- and
syringol-treated soils, as well as in a separate analysis of natural soil
fungal biomass (Supplementary Table 2
<https://www.nature.com/articles/ncomms13630#s1>). It is possible that some
SOM was derived directly from added substrates, yet mineral sorption of
monomeric compounds has largely been attributed to carboxyl groups that are
not present in our substrates45
<https://www.nature.com/articles/ncomms13630#ref45>. Instead, our data
point to SOM originating from microbes, *per se*, rather than the
substrates they utilized.

The high molecular diversity characteristic of SOM is often attributed to
the chemistry of plant-derived compounds and their decomposition byproducts
46 <https://www.nature.com/articles/ncomms13630#ref46>,47
<https://www.nature.com/articles/ncomms13630#ref47>,48
<https://www.nature.com/articles/ncomms13630#ref48>. Whether microbial
metabolites, secretions and cellular material can also produce chemically
heterogeneous SOM remains unclear48
<https://www.nature.com/articles/ncomms13630#ref48>,49
<https://www.nature.com/articles/ncomms13630#ref49>. Our results show that
microbial residues can generate SOM chemical diversity. We demonstrate that
even in the absence of chemically diverse plant inputs, our model sugar-
and syringol-treated soils have a molecular composition and diversity
resembling both soils treated with more heterogeneous plant inputs (DOC)
and natural field soils (Fig. 1
<https://www.nature.com/articles/ncomms13630?fbclid=IwAR0LJUwYyZe9DNkyb4gJtuftQzep9LTUdGHZ0ksFQdG2oXs2_k3uqU7-89U#f1>).
This highlights the influence of microbial processing on SOM chemistry
observed in soils under complex plant communities49
<https://www.nature.com/articles/ncomms13630#ref49>,50
<https://www.nature.com/articles/ncomms13630#ref50>,51
<https://www.nature.com/articles/ncomms13630#ref51>, but also that SOM
chemical heterogeneity can originate from microbial metabolism of
relatively simple C substrates such as root exudates or the soluble
components in leaf litter. Certainly, lipids, aromatics and other
plant-derived compounds may contribute directly to SOM, but we show that
microbial byproducts are also important drivers of SOM chemical
heterogeneity.

Changes in SOM chemical composition influence its stabilization potential,
interaction with clay minerals and potential responses to anthropogenic
disturbances19 <https://www.nature.com/articles/ncomms13630#ref19>,21
<https://www.nature.com/articles/ncomms13630#ref21>,23
<https://www.nature.com/articles/ncomms13630#ref23>. Thus, the differences
in SOM composition that emerged due to mineralogy could have long-term
C-cycling consequences. The underlying cause of these differences is
unclear since they could be a direct function of the clay type or due to
differences in the chemistry of microbial-derived compounds. Variations in
SOM chemistry with mineralogy have previously been attributed to
differences in dominant binding mechanisms and selective adsorption among
mineral types52 <https://www.nature.com/articles/ncomms13630#ref52>,53
<https://www.nature.com/articles/ncomms13630#ref53>. However, such
differences can also result from divergent microbial communities40
<https://www.nature.com/articles/ncomms13630#ref40>,41
<https://www.nature.com/articles/ncomms13630#ref41>, and thus the
production of chemically different microbial SOM compounds43
<https://www.nature.com/articles/ncomms13630#ref43>. Indeed, in kaolinite
soil both fungal and lipid abundances were greater relative to
montmorillonite soil. Thus, the distinct microbial communities in kaolinite
and montmorillonite and the different surface characteristics of these
minerals may together explain our observed differences in SOM chemistry.
While recent work deemphasizes the importance of plant and microbial
chemistry, *per se*, as a direct control over decomposition54
<https://www.nature.com/articles/ncomms13630#ref54>,55
<https://www.nature.com/articles/ncomms13630#ref55>, it is an important
factor in the formation of organo-mineral interactions56
<https://www.nature.com/articles/ncomms13630#ref56>. As microbial
communities and mineral types change across soils, we would expect that the
interaction between microbial input chemistry and mineral surface
properties will influence SOM dynamics.

The amount of microbial residues available for mineral stabilization also
influences SOM dynamics, and may be regulated by the efficiency of
microbial biomass production14
<https://www.nature.com/articles/ncomms13630#ref14>,15
<https://www.nature.com/articles/ncomms13630#ref15>,21
<https://www.nature.com/articles/ncomms13630#ref21>,29
<https://www.nature.com/articles/ncomms13630#ref29>. Increasingly, CUE is
being explicitly represented in models on the premise that a greater
proportion of C synthesized into biomass should lead to more microbial
residues20 <https://www.nature.com/articles/ncomms13630#ref20>,26
<https://www.nature.com/articles/ncomms13630#ref26>,57
<https://www.nature.com/articles/ncomms13630#ref57>. Yet, limited empirical
evidence directly linking CUE with microbial residues and SOC
concentrations continues to constrain model development57
<https://www.nature.com/articles/ncomms13630#ref57>. To address this, we
examined relationships between SOC concentrations, microbial community
structure and variation in microbial CUE.

We observed that soils with the highest SOC concentrations were associated
with the highest CUE and fungal abundances (Figs 3
<https://www.nature.com/articles/ncomms13630?fbclid=IwAR0LJUwYyZe9DNkyb4gJtuftQzep9LTUdGHZ0ksFQdG2oXs2_k3uqU7-89U#f3>
and 4
<https://www.nature.com/articles/ncomms13630?fbclid=IwAR0LJUwYyZe9DNkyb4gJtuftQzep9LTUdGHZ0ksFQdG2oXs2_k3uqU7-89U#f4>).
The influence of substrate on SOM accumulation is not likely a direct
function of substrate chemistry, since SOM chemistry was similar between
substrate treatments. Rather, our substrate amendments may be affecting
microbial physiologies and consequently microbial residue inputs. The
divergent communities between sugar- and syringol-treated soils likely
reflect repeated exposure to different substrates selecting for distinct
microbial communities with different CUE (Fig. 4a
<https://www.nature.com/articles/ncomms13630?fbclid=IwAR0LJUwYyZe9DNkyb4gJtuftQzep9LTUdGHZ0ksFQdG2oXs2_k3uqU7-89U#f4>
).

Syringol-treated soils consistently exhibited the highest CUE in our
experiment (Fig. 3
<https://www.nature.com/articles/ncomms13630?fbclid=IwAR0LJUwYyZe9DNkyb4gJtuftQzep9LTUdGHZ0ksFQdG2oXs2_k3uqU7-89U#f3>).
This contradicts previous arguments that an environment with more labile C
is expected to increase CUE and consequently generate more
microbial-derived SOM15 <https://www.nature.com/articles/ncomms13630#ref15>,
21 <https://www.nature.com/articles/ncomms13630#ref21>,30
<https://www.nature.com/articles/ncomms13630#ref30>. However, resource
chemistry is not the only factor that influences CUE; distinct microbial
communities may have intrinsically different CUEs that correspond with
prevailing life-history traits31
<https://www.nature.com/articles/ncomms13630#ref31>. Based on our PLFA
analysis, syringol additions selected for greater fungal abundances, which
correlates with higher CUE. Thus, the apparent substrate chemistry may be
less important for SOM accumulation than how it influences fungal abundance
and microbial CUE long term.

While our approach uses a model system without environmental constraints on
microbial biomass production such as predation and nutrient limitations
present in natural soil, it provides direct evidence that microbial biomass
can produce an abundance of stable, chemically diverse SOM. Moreover, we
are able to identify the microbial community characteristics in these
systems such as fungal abundances and CUE that influence SOM formation. Our
results point to new approaches to rebuild soil C contents that emphasize
the influence of substrate–microbe interactions on the synthesis of novel
SOM constituents that become mineral-stabilized, and will also inform the
necessary validation and parameterization of emerging microbial-explicit C
models that stress the importance of microbial residues in SOM dynamics20
<https://www.nature.com/articles/ncomms13630#ref20>,57
<https://www.nature.com/articles/ncomms13630#ref57>. Most of our
contemporary predictive models and management strategies for SOC are still
based on traditional concepts of plant chemical recalcitrance, yet the
processes that govern microbial-SOM accumulation and stabilization likely
differ from those of plant-derived SOM18
<https://www.nature.com/articles/ncomms13630#ref18>,19
<https://www.nature.com/articles/ncomms13630#ref19>,22
<https://www.nature.com/articles/ncomms13630#ref22>. For example, the
effects of elevated temperatures and soil disturbance may stimulate the
decomposition of plant-stabilized SOM but the consequences for
microbial-stabilized residues are less clear. It is likely that
environmental changes such as soil warming influence microbial-SOM, not
necessarily by destabilizing it, but via alterations in the capacity of the
microbial community for efficient residue production30
<https://www.nature.com/articles/ncomms13630#ref30>. Moreover, our data
provide critical empirical insights to advance new microbial-explicit
biogeochemistry models, including how microbe–substrate interactions
influence SOM dynamics. Thus, our results provide new perspectives on the
origin of stable SOM that will help foster the progress needed to model and
manage soils to promote enhanced SOM accumulation.
Methods
Experimental conditions and sampling

Laboratory mesocosms consisted of 100 g dry wt of 33% kaolinite or
montmorillonite clay mixed with quartz sand. The cation exchange capacity
was 5.2 and 28.6 meg per 100 g soil for the kaolinite and the
montmorillonite soil mixtures, respectively. Metal concentrations for the
kaolinite mixture were 5.6% Al, 1% K, 0.9% Fe, 0.14% Ca, 0.0.11% Na and
0.06% Mg. Montmorillonite mixture metal concentrations were 2.2% Al, 0.8%
K, 2.3% Fe, 0.39% Ca, 0.14% Na and 0.32% Mg. Soil mixtures received weekly
additions of glucose, cellobiose, syringol or switchgrass plant DOC (0.7 mg
C per g dry soil) and biweekly additions of a multi-nutrient Hoagland’s
solution (0.023 mg N per g dry soil). The substrate-C (5 ml of 14,000 ppm
C) and nutrient additions (0.5 ml of 4,666 ppm N) were syringe-injected
throughout the model soils to facilitate uniform substrate application. The
rate of weekly C additions reflects the upper range of natural C inputs to
soil from daily root exudations. Total C inputs from added substrates over
the course of 15 months amounted to 46.9 mg C per g soil and total
inorganic N from nutrient solution was 1.56 mg N per g soil. To create DOC,
we mixed 150 g switchgrass (*Panicum virgatum*) tissue (leaf, stem and
seed) with 2 l H2O and shook for 24 h before filtering. The filtrate was
then partially evaporated until the C concentration was 18 mg C per ml (C:N
20). We created the microbial soil inoculum in a soil slurry (1:100 w/v)
using soil collected from a Michigan bromegrass (*Bromus inermis*) field at
0–7 cm depth. We then syringe-injected 100 μl of inoculum solution (∼0.00001,
g natural soil per g model soil) into the soil mixture. Each soil mesocosm
treatment was replicated five times with five destructive harvests at 6, 9,
12, 15 and 18 months. During incubation, soils were maintained at 25 °C,
45% water holding capacity, and a pH between 4.6 and 5. The pH of
DOC-treated soils, however, increased to 7 after a few months of incubation
and therefore DOC biological data are minimally included in our analyses.
During the 15-month incubation period no soil mixing occurred in order to
better simulate an undisturbed soil environment and to minimize aggregate
destruction.

After 15 months, substrate and nutrient additions ceased but soils were
maintained for an additional 3 months under the previous incubation
conditions with periodic hand mixing to facilitate microbial uptake of any
remaining substrate or unstabilized microbial biomass. Microbial community
activity was monitored weekly by CO2 flux measurements for the first 9
months and then biweekly thereafter, and by microbial hydrolytic and
oxidative potential enzyme activities58
<https://www.nature.com/articles/ncomms13630#ref58> at each mesocosm
harvest.
SOM chemistry and concentrations

We assessed SOM chemistry using pyrolysis-GC/MS59
<https://www.nature.com/articles/ncomms13630#ref59>. Compound peaks were
analysed and identified with the Automated Mass Spectral Deconvolution and
Identification Systems (AMDIS V 2.65) software, the National Institute of
Standards and Technology (NIST) compound library and published literature59
<https://www.nature.com/articles/ncomms13630#ref59>. Organic matter
compounds are expressed as the % relative abundance of total sample peak
area and classified based on origin (lipids, lignin derivatives,
polysaccharides, proteins, non-protein N-bearing and phenolics).
Identifiable compounds with potentially multiple origins were classified as
unspecified. Aromatic compounds also with an unspecified origin were
classified as aromatics. The SOM chemical diversity was determined based on
the total number of identifiable compounds that were >1% relative abundance.

Total SOC concentrations were determined on an elemental analyser (Costech
ECS 4010). Total microbial biomass carbon (MBC) was determined by the
difference in C concentration (using a Shimadzu TOC-L analyser) in
chloroform fumigated and unfumigated soil subsamples extracted with 0.5 M K2
SO4. We calculated the total SOM conversion efficiency as the amount of
total SOC relative to the total substrate-C added for each destructible
harvest, where the total substrate added is the sum of the weekly substrate
addition within the time period up until harvest.
SOM stability

We determined biological stability in a separate 3-month incubation on soil
subsamples harvested at 6 months. During this separate incubation
experiment, soils received three applications of 13C-labelled 1:1 glutamic
acid/glucose 25 atom % mixture at 50 μg C per g soil. The labelled
substrate enabled us to use a standard isotope mixing model35
<https://www.nature.com/articles/ncomms13630#ref35> to determine the amount
of newly formed C vulnerable to decomposition by an active microbial
community. During the 3-month incubation, soils were frequently hand-mixed
to facilitate SOM mineralization and maintained under the same incubation
conditions as the long-term incubation. We also assessed the chemical
stability of the accumulated SOM with acid hydrolysis fractionation37
<https://www.nature.com/articles/ncomms13630#ref37>. After the full 18
months incubation, 1 g soil was treated with 50 ml 6 M HCl and heated at
100 °C for 18 h. After heating, the acid hydrolysable fraction was decanted
and the remaining non-acid hydrolysable (i.e., stable) residue was rinsed
four times with DI-H2O and isolated by centrifugation36
<https://www.nature.com/articles/ncomms13630#ref36>,37
<https://www.nature.com/articles/ncomms13630#ref37>. The chemically stable
C was then determined based on the differences in C remaining in the
non-acid hydrolysable residues and the initial C content of the sample.
Microbial community composition

We determined differences in microbial community composition from PLFA
biomarkers, identified and quantified at 12 and 15 months60
<https://www.nature.com/articles/ncomms13630#ref60>. We included a
phosphate buffer in the single-phase solvent system (chloroform) to extract
only viable microorganisms. Lipid extracts were fractionated on silicic
acid columns to isolate and collect polar lipids. Polar lipids were
methylated to fatty acid methyl esters with 0.2 M methanolic KOH, purified
and then analysed on a Varian 3800 GC-FID. Peaks were quantified using
internal standards and identification was based on retention time data with
known standards. Fungal relative abundance was determined by the sum of
polyenoic unsaturated fatty acids (18:2ω6 and 18:1ω9c), Gram-positive
relative abundances by the sum of the total branched, saturated fatty acids
(i15:0, a15:0, i16:0, i17:0 and a17:0) and Gram-negative relative
abundances by the sum of monoenoic and cyclopropane unsaturated fatty acids
(16:1ω7c, 16:1ω7t cy17:0, 18:1ω7c and cy19:0). We did not detect any
actinobacterial fatty acids.
Soil microbial CUE and enzyme activities

We characterized microbial CUE at 9 and 15 months by measuring the amount
of a 13C-labelled glutamic acid incorporated into MBC and respired as 13CO2–C
(refs 21 <https://www.nature.com/articles/ncomms13630#ref21>, 30
<https://www.nature.com/articles/ncomms13630#ref30>). Fresh soil subsamples
were amended with 25 atom% labelled 13C glutamic acid (50 μg C per g dry
soil,<1% total soil C) and incubated at 25 °C for 22 h. The 22-h incubation
time was based on preliminary respiration curves to determine the period
when the majority of glutamic acid is utilized but before substrate
recycling begins. Following incubation, soils were extracted for 13C-glutamic
acid incorporation into MBC by extracting and analysing MBC as discussed
previously and determining its isotopic composition on a 1030 TOC Analyzer
(OI Analytical, College Station, TX) interfaced with a PDZ Europa 20–20
isotope ratio mass spectrometer. A 12-ml CO2 sample was also collected for
determining 13CO2–C respiration on a GC-isotope ratio mass spectrometer
(ThermoScientific, Bremen, DE).

This approach estimates the amount of C allocated to biomass per unit of C
substrate consumed and represents a proxy for microbial CUE. We calculated
CUE as [MB13C/(MB13C+13CO2-C) × 100], where MB13C and 13CO2-C were
determined using a standard isotope mixing model equation29
<https://www.nature.com/articles/ncomms13630#ref29> and represent the
amount of substrate incorporated into MBC and the substrate-C respired as CO
2, respectively. We used glutamic acid to estimate microbial community CUE
across all substrate-treated soils since it is taken up directly into
microbial cells. This approach allows us to more directly compare CUEs
across treatments since substrate-C allocation towards enzyme production is
minimized and we can better approximate the efficiency of new biomass
growth rather than substrate degradation efficiency32
<https://www.nature.com/articles/ncomms13630#ref32> (Supplementary Note 2
<https://www.nature.com/articles/ncomms13630#s1>).

The potential activities of microbial extracellular enzymes were measured
at 6, 9, 12 and 15 months of incubation. We used a standard fluorometric
method to assess hydrolytic enzyme activity by adding 1 g soil dry weight
homogenized in a slurry with 50 mM sodium acetate buffer58. The buffer
solution was adjusted to the average soil pH of 4.9. Soil homogenates were
added to black, 96-well microplates with compound-specific fluorescing
substrates bound to methylumbelliferone. Oxidative enzyme activity (phenol
oxidase and peroxidase), associated with lignin breakdown, was measured
spectrophotometrically using clear 96-well microplates.
Statistical and data analyses

Data were analysed using a linear mixed-model two-way analysis of variance
(ANOVA) to determine differences in SOM chemistry, SOC concentration, SOC
conversion efficiency, SOC stability, PLFAs and CUE between clay mineralogy
and substrate type. Treatment replicate (*n*=5) was used as a random
effect, and clay and substrate were treated as fixed effects. Since the
experimental design was unbalanced (there was no syringol treatment within
kaolinite soils), analyses for differences in main effects of substrate and
mineralogy and their interactions did not include syringol. A one-way ANOVA
was used to examine the effect of substrate within a clay treatment,
allowing us to include syringol-treated soils in our comparisons. In many
analyses the glucose and cellobiose treatments were not statistically
different (*P*>0.05). In these cases, glucose and cellobiose data were
averaged and reported as a ‘sugar’ treatment. Pair-wise comparisons between
treatments were determined by Tukey’s HSD (*P*<0.05). We used Pearson’s
correlation analysis to examine relationships between SOC concentrations,
microbial CUE, microbial community composition and SOM chemistry. All ANOVA
and correlation analyses were performed in SAS v.9.3 (SAS Institute, 1999)
using PROC MIXED and PROC CORR. When necessary, data were normalized using
log transformation before analysis of variance analyses. Significance for
all analyses was determined at a probability level of *P*<0.05 unless
otherwise stated. Variation around means are reported or shown as standard
error.

We also used non-metric multidimensional scaling (NMDS) (PC-ORD; version
4.14) to explore differences in SOM chemistry and community composition.
Pyrolysis-gas chromatography/mass spectrometry compound relative abundances
and PLFA biomarkers were relativized to maximum and used separately in NMDS
matrices. We also used NMDS to determine relevant parameters influencing
SOC concentrations for use in our correlation analyses, where SOC
concentrations at 9, 12, 15 and 18 months were grouped *a priori* into low,
medium and high SOC concentrations. The Sorensen (Bray-Curtis) index was
used as a distance measure for all ordinations. Final NMDS solutions were
considered acceptable if Monte Carlo simulations had stress values <20 and
a solution stability of <0.005. We determined significant groupings by the
Monte Carlo stress reduction (*P*<0.05) and multi-response permutation
procedure (*P*<0.05).
Data availability

The data that support the findings of this study are available on request
from the corresponding author (C.M.K.).
Additional information

*How to cite this article:* Kallenbach, C. M. *et al*. Direct evidence for
microbial-derived soil organic matter formation and its ecophysiological
controls. *Nat. Commun.* *7,* 13630 doi: 10.1038/ncomms13630 (2016).

*Publisher’s note*: Springer Nature remains neutral with regard to
jurisdictional claims in published maps and institutional affiliations.

*Disclaimer*

Any opinions, findings and conclusions or recommendations expressed in this
material are those of the authors and do not necessarily reflect the views
of the National Science Foundation or the DOE.
Change history

   - 24 September 2018

   In the originally published version of this Article, financial support
   was not fully acknowledged. The PDF and HTML versions of the Article have
   now been corrected to include support from the NSF Long-term Ecological
   Research Program (DEB 1637653) at the Kellogg Biological Station and from
   Michigan State University AgBioResearch.

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Acknowledgements

This work was supported by the National Science Foundation Doctoral
Dissertation Improvement Grant to C.M.K. (DEB-1311501). Partial funding for
this research was also provided by the Department of Energy’s Great Lakes
Bioenergy Research Center (DOE Office of Science, DE-FC02-07ER64494 and
Office of Energy Efficiency and Renewal, DE-ACO5-76RL01830) and by the New
Hampshire Agricultural Experiment Station (scientific contribution number:
2605; USDA-NIFA Hatch project: 230340). We are grateful to Cory Cleveland
and Phil Robertson for providing comments on previous manuscript drafts.
Support for this research was also provided by the NSF Long-term Ecological
Research Program (DEB 1637653) at the Kellogg Biological Station and by
Michigan State University AgBioResearch. Disclaimer Any opinions, findings
and conclusions or recommendations expressed in this material are those of
the authors and do not necessarily reflect the views of the National
Science Foundation or the DOE.
Author information
Affiliations

   1. Department of Natural Resources and the Environment, University of
   New Hampshire, Durham, New Hampshire 03824, USA
      - Cynthia M. Kallenbach
      - , Serita D. Frey
      -  & A. Stuart Grandy
   2. Soil and Crop Sciences Department, Colorado State University, Fort
   Collins, Colorado 80523, USA
      - Cynthia M. Kallenbach

Contributions

C.M.K. and A.S.G. conceptualized the study. C.M.K. collected the data and
performed all the statistical analyses. All authors contributed to
manuscript development and revisions.
Competing interests

The authors declare no competing financial interests.
Corresponding author

Correspondence to Cynthia M. Kallenbach
<https://www.nature.com/articles/ncomms13630/email/correspondent/c1/new>.
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